The last challenge mentioned in the earlier section is a prevailing issue for many languages around the world.
Low-resource languages are natural languages with little or no digital data or resources [1]}, [2]}.
This challenge has meant that so many languages are unrepresented in many deep ml models, as they usually require a lot of data for pretraining.
Even
Noteworthy, though, that multilingual versions of some of the models are being made with very limited data of the low-resource languages.
They are, however, known to have relatively poor performance compared to models trained completely on the target language data [3]}, [4]}, [5]} and only few languages are covered [2]}.
Approaches to mitigating this particular challenge involve human and automatic mt attempts [1]} and efforts at exploiting cross-lingual transfer to build conversational ai capable of machine-machine conversations for automated data generation [2]}.
